Apr 28, 2025

US-1: Full AI Nationalization can cause Misaligned Economic Incentives

Ryan Tovcimak, Nikolay Radev

Details

Details

Arrow
Arrow
Arrow
Arrow
Arrow

The escalating geostrategic importance of frontier AI development increases the likelihood of nationalization. While no explicit plans have emerged in the United States, such action would likely be swift and comprehensive. A government seizure of critical AI infrastructure would fundamentally transform the sector's economic foundation – shifting funding from traditional private sources to the American tax base, thereby repositioning AI as a public good. The objectives driving development would similarly pivot from user engagement to national security imperatives. Given the history of American adversaries pursuing intellectual property theft, this transition would likely establish a more restrictive diffusion model that prioritizes security over openness. By tightly controlling crucial elements of the AI stack, that approach risks diminishing the broader societal benefits that might otherwise emerge from AI advancement.

Cite this work:

@misc {

title={

US-1: Full AI Nationalization can cause Misaligned Economic Incentives

},

author={

Ryan Tovcimak, Nikolay Radev

},

date={

4/28/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

Arrow
Arrow
Arrow
Arrow
Arrow

Joel Christoph

The paper offers a timely policy analysis of a full United States nationalization of frontier AI labs and argues that such a move could create misaligned economic incentives that slow diffusion and reduce overall welfare. It surveys historical precedents like the USRA, Manhattan Project, Apollo Program, MITI, and Korea’s heavy-industry drive, then applies public-choice theories such as Niskanen’s budget maximizing bureaucracy and Kornai’s soft budget constraint to foresee cost overruns and efficiency losses. The narrative is well structured and the prose is clear. The inclusion of concrete channels like talent retention, compute commandeering under the Defense Production Act, and security driven restrictions on collaboration grounds the discussion in plausible mechanisms. The historical vignettes and theories are drawn together coherently and the paper ends with pragmatic recommendations that nationalization should be a last resort in favor of “soft” public-private control. ​

The contribution is mainly descriptive and lacks formal modeling or new empirical evidence. No quantitative framework is provided to compare nationalized and private incentive structures, nor are there back-of-the-envelope fiscal estimates beyond citing past GDP percentages for the Manhattan and Apollo projects. The historical cases are summarized but not tested for external validity in the AI context. Recent literature on compute governance, state capacity in technology races, and AI alignment economics is largely missing, so the intellectual foundation rests on a limited set of classic public-choice sources.

AI safety relevance is present but indirect. The paper stresses that misaligned incentives under nationalization could hinder diffusion and perhaps heighten safety risks, yet it does not trace how a public monopoly would affect catastrophic misuse probabilities, alignment R&D funding, or global compute races. A more explicit mapping from ownership structure to safety outcomes would strengthen the impact.

Technical quality and documentation are modest. The essay is properly referenced and the parsed PDF contains tables and a Bloomberg chart, but no data, code, or appendices accompany the narrative, making replication or further analysis impossible. The policy recommendations are sensible yet untested and rely on qualitative reasoning alone.

Mar 11, 2025

AI Safety Escape Room

The AI Safety Escape Room is an engaging and hands-on AI safety simulation where participants solve real-world AI vulnerabilities through interactive challenges. Instead of learning AI safety through theory, users experience it firsthand – debugging models, detecting adversarial attacks, and refining AI fairness, all within a fun, gamified environment.

Track: Public Education Track

Read More

Mar 10, 2025

Attention Pattern Based Information Flow Visualization Tool

Understanding information flow in transformer-based language models is crucial for mechanistic interpretability. We introduce a visualization tool that extracts and represents attention patterns across model components, revealing how tokens influence each other during processing. Our tool automatically identifies and color-codes functional attention head types based on established taxonomies from recent research on indirect object identification (Wang et al., 2022), factual recall (Chughtai et al., 2024), and factual association retrieval (Geva et al., 2023). This interactive approach enables researchers to trace information propagation through transformer architectures, providing deeper insights into how these models implement reasoning and knowledge retrieval capabilities.

Read More

Mar 10, 2025

LLM Military Decision-Making Under Uncertainty: A Simulation Study

LLMs tested in military decision scenarios typically favor diplomacy over conflict, though uncertainty and chain-of-thought reasoning increase aggressive recommendations. This suggests context-specific limitations for LLM-based military decision support.

Read More

This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.